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REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
doi: 10.7213/rebrae.10.003.AO06
Enterprise Multiple and Future Returns of the
Brazilian Stock Market
Rafael Igrejas[a], Raphael Braga da Silva[b], Marcelo Cabus Klotzle[c], Antonio Carlos
Figueiredo Pinto[d], Paulo Vitor Jordão da Gama Silva[e]
[a] Professor at Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro – RJ, Brazil – E-mail:
rafael.igrejas@iag.puc-rio.br
[b] Professor at Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro – RJ, Brazil, E-mail:
raphael.braga@prof.iag.puc-rio.br
[c] Professor at Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro – RJ, Brazil, E-mail:
klotzle@iag.puc-rio.br
[d] Professor at Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro – RJ, Brazil, E-mail:
figueiredo@iag.puc-rio.br
[e] Professor at Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro – RJ, Brazil, E-mail:
rjdagama@hotmail.com
Abstract
The estimation of cross-section returns for defining investment strategies based on financial multi-
ples has been proven to be relevant following Fama and French’s (1992) research. One of the chal-
lenges for such studies is to identify the main variables that are suitable for explaining the returns
in a particular context because the variables that are widely used in developed markets behave
differently in emerging countries. In this study, we analyze the predictive power of the EV/EBITDA
multiple in the context of the Brazilian stock market. The results show that the analyzed multiple
has a strong relationship with the future returns of companies listed on the BM&F BOVESPA index
between 2005 and 2013. For the period under review, the investment strategy of purchasing stocks
when EV/EBITDA was low and selling stocks when EV/EBITDA was high showed abnormal returns
of 15.94% per year, even after controlling for risk factors.
Keywords: Enterprise multiple; expected returns; risk factors; cross-section.
IGREJAS, R. ET AL.
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432
Introduction
The estimation of cross-section returns for defining investment strategies based
on enterprise multiples is greatly needed to assess stock markets in emerging coun-
tries. In this context, the enterprise value (EV)/earnings before interest, taxes, depreci-
ation, and amortization (EBITDA) multiple (the EV/EBITDA multiple) may prove effi-
cient and is recognized to be a relevant pricing tool. This multiple is also referred by
the acronym EM (enterprise multiple). Despite its significant spread throughout the
capital market, the EM variable (as the enterprise value multiple will be referred to in
this paper) is little explored with regard to estimating portfolio returns using cross-
sectional regressions, especially in Brazil, where there are no extant studies involving
this approach.
The EM variable is obtained by dividing the enterprise value (market value of
the common shares + debt + market value of the preferred shares – cash) by the
EBITDA (operating income before depreciation and amortization). There are some
advantages to using the EM variable to estimate expected returns instead of other
more popular multiples, such as price/earnings. First, it is possible to include compa-
nies with different capital structures, i.e., with different levels of leverage, in the sample
since a company's value includes the value of debt. In addition, operating income
measured according to EBITDA has the advantage of not being affected by non-
operating income; thus, it is a more appropriate metric for measuring operating per-
formance. Another important point regarding the use of the EM variable is related to
the construction of a portfolio of assets that simulates future investment returns. Intui-
tively, the negotiation of this portfolio can be interpreted as the negotiation of a portfo-
lio combining debt and equity.
Although recent academic studies recognize the EM variable to be relevant,
questions remain about the behavior of the expected return of stocks in Brazil when it
is measured as a function of this variable.
The present study uses different methodologies commonly used to identify the
presence of abnormal returns associated with the investment strategy based on the EM
variable, including the formation of long and short portfolios and the estimation of the
Fama and French (1993) and Carhart (1997) models for the Brazilian market, compar-
ing them to emerging markets. In addition, this study evaluates the robustness of the
EM variable with regard to cross-sectional regressions when the following factors are
included: the small minus big (SMB) factor, which is calculated as the difference be-
tween the returns of the smallest and largest companies in terms of market value; the
high minus low (HML) factor, i.e., the difference in returns between the companies with
the 30% highest and those with the 30% lowest book-to-market values; and the up
minus down (UMD) factor, which is known as momentum factor and is calculated by
subtracting the equal weighted returns of higher performing companies from the equal
weighted returns of lower performing companies.
The results of 96 cross-sectional monthly regressions using the stocks listed on
the São Paulo Stock Exchange (BOVESPA) between January 2005 and July 2013 con-
firmed a negative relationship between the EM variable and returns. As expected, the
EM variable was found to be robust for stock portfolios belonging to the BM&F BO-
VESPA Index (IBOVESPA), confirming the hypothesis that the expected return of stocks
in Brazil during the study period decreased with the EM. Thus, stocks with high EMs
Enterprise multiple and future returns of the Brazilian stock market
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
433
displayed lower rates of return as compared to those with low EMs. A strategy of buy-
ing shares with a low EM and selling shares with a high EM during this period—given
that the assets in the portfolio have equal weight—would result in an abnormal return
of 1.24% per month. That is, even after controlling for the factors of market premium
(MKT), size (SMB) and book-to-market (HML), this investment strategy would achieve
an annualized return of 15.94%.
The paper is structured as follows. In section two, a review of the literature on
asset pricing and returns analysis is presented. Subsequently, the data processing pro-
cedure and the statistical summary are presented in detail in Section 3. This is followed
by a description of the procedures and the methodology employed in the cross-section
regressions in Section 4. In Section 5, the performance of the portfolios formed using
the EM variable is analyzed, and the results are comparatively analyzed in Section 6
with the results reported in prior studies. Section 7 concludes the paper.
Theoretical background
The research on asset pricing began with Sharpe (1964), Lintner (1965), and
Black and Scholes (1973), which were followed by studies that reiterated the im-
portance of understanding the markets’ movements through cross-sectional analysis.
Banz (1981) is noteworthy for adding the size effect as a relevant variable for explain-
ing the average stock returns—the returns of smaller companies (those with low mar-
ket value) would be higher than the larger companies.
Basu (1983) used the ratio of return to share price, the size variable, and the
market’s beta (β) for explaining the returns of the shares of U.S. companies. Rosenberg,
Reid, and Lanstein (1985) showed that the average U.S. companies’ return of the shares
was positively related to the ratio of equity and market value of shares. Moreover,
Chan, Hamao, and Lakonishok (1991) found evidence that the book-to-market multiple
showed strong significance in explaining the average return of the Japanese stock mar-
ket.
Fama and French’s (1992) study provided a great stimulus to the literature on
the use of indicators as valuation measures. This study documented the importance of
the size and book-to-market variables for the expected stock returns. Further, Fama
and French (1993) showed that the average market returns could be explained using
factors that are obtained based on the market premium, the size effect, and the book-
to-market ratio.
In the stock markets of emerging countries, different studies have explored
cross-section returns in order to estimate the stock returns or the implementation of
investment strategies. One of the challenges for these studies was the identification of
relevant variables for explaining returns, given that the variables that are widely used
in developed markets have different behaviors in emerging countries.
Harvey (1995) pioneered the use of cross-sectional regressions to evaluate re-
turns in emerging markets, identifying low and non-significant betas as relevant fac-
tors. There is evidence of a premium for stocks with a high value in small companies in
emerging markets (Patel, 1998; Rouwenhorst, 1999). Claessens, Dasgupta and Glen
(1995) showed that the portfolio returns in these markets generally do not display
normal behavior, using non-significant beta to explain the returns. Further, they ob-
served the size effect to be non-significant and reported strong evidence of the signifi-
IGREJAS, R. ET AL.
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
434
cance of the book-to-market variable. Hart, Slagter and Dijk (2001) observed no signifi-
cance for the size effect; however, there was a significant change in the direction of the
effect when a minimum value of market capitalization was imposed. Fama and French
(1998) pointed out that the difference between the periods used in the study samples,
the different methodologies used, and the presence of outliers could explain the differ-
ences between the results of studies in emerging market contexts and the results iden-
tified in their seminal articles that used U.S. market data. In a recent study in Brazil,
Silva et al. (2012) identified low statistical significance for the size and book-to-market
variables when estimating stock returns using cross-sectional regressions.
Kim and Ritter (1999) observed that although the valuation metrics presented
significant deficiencies, the performance of the EM variable is as satisfactory as the
price/earnings multiple when evaluating mature companies in the market. In addition,
the EM variable has the advantage of using EBITDA as a return variable. As stated by
Koller, Goedhart and Wessels (2005), operating income is not affected by non-
operating gains or losses. Therefore, operating income before depreciation is a more
accurate and less easily manipulated measure of profitability compared to net profit. In
practice, the EM variable allows companies to be compared across industries or within
an industry.
Cochrane (1991) and Liu, Whited and Zhang (2009) developed models that are
quite similar to the EM. In these studies, the return on investment was considered to be
the weighted average of stock returns and the return of corporate bonds after tax, also
known as the weighted average cost of capital (WACC). The WACC - which is recog-
nized as the unlevered return on investment - can also be understood as EBITDA/EV,
which happens to be the inverse of the EM variable. By rearranging the terms, the stock
returns become the leveraged return on investment. As a result, the WACC and the EM
variable are positively related to the cost of equity. By definition, companies with a
high EM carry a high value for every penny of operating income compared to compa-
nies with a low EM coefficient. In other words, companies with a high EM have greater
growth opportunities, lower cost of capital, and, therefore, lower expected return com-
pared to companies with a low EM.
Given the importance of the EM variable to market analysts, Damodaran (2010)
incorporated this variable in the analysis, together with the price/earnings and
price/sales multiples. This allows the comparison of companies with different levels of
leverage and includes the value of debt in the composition of the variable.
Loughran and Wellman (2012) evaluated the relationship between the EM vari-
able and expected stock returns between 1963 and 2009 in the U.S. Even after control-
ling for size, they found that companies with a low EM obtained higher returns (by
more than 5% per year) compared to companies with a high EM.
The present study aims to test the relationship between the enterprise multiple (EM)
and stock returns in the Brazilian market using the three-factor model proposed by
Fama and French (1993) and the four-factor model proposed by Carhart (1997) in
cross-sectional monthly regressions.
Methodology
The analysis and processing of the data for this study was based on the method-
ology proposed by Fama and French (1992). The sample included all the stocks listed
Enterprise multiple and future returns of the Brazilian stock market
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
435
on the São Paulo Stock Exchange (BOVESPA) between January 2005 and July 2013. The
relevant financial information for this period was obtained from the Bloomberg data-
base. The first sample included 330 companies; only the most liquid stock of each com-
pany was selected. To avoid any bias in the sample, the following companies were ex-
cluded from the analysis: all financial companies; companies that did not present con-
secutive quotes in the 12 months prior to the portfolio construction; and companies
with a book value lower than or equal to zero on December 31 of year t-1.
After implementing the stock filtering criteria, a mean of 140 shares per portfo-
lio was obtained for this period, as shown in Table 1.
Table 1: Number of Stocks in the Sample
Period
Jul 05-Jun
06
Jul 06-Jun
07
Jul 07-Jun
08
Jul 08-Jun
09
Jul 09-Jun
10
Jul 10-Jun
11
Jul 11-Jun
12
Jul 12-Jun
13
Number
of
Shares
68
88
118
130
168
177
184
184
Source: Authors.
To perform the analysis based on the Fama and French (1992) model, the port-
folios were created in June of year t, and the stock returns from July of year t to June of
year t + 1 were evaluated. The stock returns and the market value were extracted in
monthly terms. The EM variable in this study was calculated as the ratio of enterprise
value (EV) and EBITDA for year t - 1 to the portfolio formation date. In order to main-
tain consistency with the Fama and French model (1992) model, companies with a
negative EBITDA value were excluded, since this condition is associated with reduced
market value on average, a low return in the previous year, and a low return in subse-
quent years.
Table 2 presents the statistical summary of the 1,117 stocks observed in the
portfolio between 2005 and 2012; this table provides a brief analysis of the character-
istics of the portfolio. The average market value obtained was R$ 8,926.17 million, with
a median of R$ 2,223.82 million and with a maximum value achieved of R$ 355,779.06
million. The subsequent average return was 0.48% per year, and the mean value for
the EM variable was 11.28, i.e., on average, investors pay R$ 11.28 in debt and shares
for each unit of EBITDA. The sample comprises companies with a minimum book-to-
market of 0.02 and companies that potentially reach a book-to-market value of 26.87.
Table 2: Summary Statistics: 2005–2013
Statistics
Market Cap.
(R$ MM)
Enterprise multiple
(EM)
Book to Mar-
ket
Accumulated
Returns
Subsequent
year return
Mean
8,794.82
57.43
0.92
11.9%
0.46%
Minimum
3.77
-52.88
0.02
-81.3%
-13.66%
25th
674.08
5.31
0.34
-18.2%
-1.78%
Median
2,224.43
7.91
0.59
6.5%
0.69%
75th
6,795.12
13.01
1.07
35.0%
2.50%
Maximum
355,779.06
11,749.65
26.87
193.0%
57.89%
Source: Authors.
IGREJAS, R. ET AL.
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
436
Results
Cross-Section Regressions
Considering the EM variable as a relative measure of value, we sought to observe
the performance of this variable based on the results of the cross-section regressions
presented in Table 3. We used book-to-market as a control variable since it is common-
ly used as a measure of value. The dependent variable is the monthly return without
weighting (equal weighted) of company i in calendar year j. The independent variables
are size (market value in June of year t), book-to-market (book value of the net assets
of the previous year divided by market value as of December in year t - 1), time (cumu-
lative returns between months j - 12 and month j - 2), and EV/EBITDA as of December
in year t - 1.
Table 3 presents the mean coefficients and the t-statistic obtained in the 96
monthly regressions using stocks listed on BOVESPA between January 2005 and July
2013. The first regression reproduces the Fama and French (1992) model, which
serves as the foundation for analyzing stock returns in Brazil; this regression will be
used for a comparative analysis with other studies. When analyzing the size variable,
the value for the t-statistic is found to be -1.036, which differs in magnitude from the
value reported by Fama and French (1992); however, the negative sign is maintained,
in line with the seminal article. On the other hand, the book-to-market (BTM) present-
ed a t-statistic with a value of 2.242, demonstrating the same positive relationship as in
the seminal study (although the magnitude of the coefficient was lower in this study).
Despite some divergence when compared to the results of studies involving U.S. com-
panies, the values obtained from Regression 1 approximate the results reported in
studies using stocks from emerging market, as will be discussed in Section 6. When the
time variable proposed by Carhart (1997) is added in Regression 2, the significance
level of the size variable remains unchanged. However, it is clear that beyond the book-
to-market, the time variable becomes significant, with a t-statistic of 1.881.
In Regression 3, the EM variable was introduced as the only independent varia-
ble. For this variable, the market value term is in the numerator, while the accounting
term is in the denominator, demonstrating its inverse relationship with the book-to-
market. Although negative, the EM variable is significant, with a t-statistic of -1.883.
Regression 4 included the size (MKT CAP), book-to-market (BTM), momentum (MOM),
and enterprise multiple (EM) variables. Except size, all the other variables proved to be
significant at 5%.
Table 3: Cross-Section Regressions of Monthly Returns on Market Capitaliza-
tion, Book-to-Market, Prior Return, and Enterprise Multiple: 2005—2013
0 1 2 3 4
ln( ) ln( ) ln( ) ln( )
ij j j ij j ij j ij j ij ij
a a MKT CAP a BTM a MOM a EMr e
Model
Intercept
MKT CAP
BTM
MOM
EM
1
Coef
0.019
-0.001
0.004
t-stat
1.666
-1.036
2.242
2
Coef
0.019
-0.001
0.004
0.009
t-stat
1.516
-1.089
2.301
1.881
3
Coef
0.015
-0.003
t-stat
2.701
-1.883
4
Coef
0.025
-0.001
0.003
0.009
-0.002
t-stat
1.988
-1.135
2.076
1.780
-1.685
Source: Authors.
Enterprise multiple and future returns of the Brazilian stock market
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
437
The negative relationship between the EM variable and returns proves to be ro-
bust and significant, even after controlling its effects using the size, book-to-market,
and momentum variables. This finding confirm the hypothesis that the expected stock
returns in Brazil during the study period would decrease with the enterprise multiple.
In other words, stocks with a high EM display a lower rate of return compared to com-
panies with a low EM, which is in line with the results reported by Loughran and
Wellman (2012) for the U.S. market.
It is important to note that even though the book-to-market and EM variables
are highly correlated, they are able to predict future returns when they are used to-
gether, indicating that the EM variable explains a part of the expected return that is not
captured by the book-to-market.
However, in the regression analysis, interpreting the book-to-market and EM co-
efficients becomes problematic, since these tend to be highly correlated. To work
around this limitation, it is necessary to apply Fama and French’s (1993) portfolio
formation approach, since the creation of factors inhibits correlation between varia-
bles.
Portfolios Using em Variable
To estimate the degree to which an investor can obtain future returns by follow-
ing an investment strategy based on the portfolios of companies with high and low EM
coefficients, we use the Fama and French (1993) approach to construct portfolios
based on the EM variable.
The companies in the sample were divided into quintiles according to the annual
EM values every June from 2005 to 2013. In Panel A of Table 4, equal weighted average
monthly returns of 1.29% are observed for companies in the quintile of firms with
lower EM; therefore, these are considered to be value companies. Equal weighted aver-
age monthly returns of 0.09% are obtained for companies in the quintile with a high
EM, which are considered to be growing businesses. This means that an investment
strategy involving the purchase of value stocks (low EM) and the sale of growth stocks
(high EM) would guarantee a return of 1.20% per month (15.39% per year) during this
period.
When considering the returns weighted by market value (Panel A of Table 4), al-
so known as value weighted returns, the quintiles of firms with the lowest and highest
EM have mean returns of 1.04% and 0.30%, respectively. Thus, although an investment
strategy that considers the weight of the shares at market value provides a lower re-
turn than the strategy without weighting, it would still be possible to obtain a monthly
return of 0.74% over the period (9.25% per annum).
Panel B of Table 4 presents the results of the regressions when the following fac-
tors are used: (i) MKT, calculated as the difference between the returns of IBOVESPA
and CDI; (ii) SMB, which is the difference between the returns of the smaller and those
of the larger companies in terms of market value; (iii) HML, calculated as the difference
in returns between the companies with the 30% highest and those with the 30% low-
est book-to-market values; and (iv) UMD, which is the time factor. Thus, Panel B as-
sesses whether the returns earned by the investment strategy based on the EM varia-
ble are robust with regard to the factors proposed by Fama and French (1993) and
Carhart (1997). In the regressions, the dependent variable is the monthly return ob-
IGREJAS, R. ET AL.
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
438
tained by calculating the difference between the monthly returns of the value compa-
nies (low EM) and the returns of the growth companies (high EM).
The first three rows of Panel B present Models 1–3, which use returns with equal
weights (EW). In Models 4–6, the returns are weighted by the company’s market value.
The t-statistic is calculated based on the standard error, and the Newey-West estima-
tor, which can be seen in White (1980), is used for correcting heteroscedasticity and
serial autocorrelation.
Table 4: Value-Growth Portfolio Based on EM Variable
Panel A: Monthly Returns for EM Quintiles
Quintile
EW Returns
VW Returns
Low
1.29%
1.04%
2
0.98%
0.36%
3
0.82%
0.35%
4
0.77%
0.68%
High
0.09%
0.30%
Value-
Growth
1.20%
0.74%
Panel B: Monthly EM Quintile Returns (value-minus-growth) as the Dependent Variable
Models
Weighting Method
Alpha
MKT
SMB
HML
UMD
Adjust R2.
MOD 1
EW
coefs
0.0135
-0.0867
0.0361
t-stats
2.2057**
-2.3981**
MOD 2
EW
coefs
0.0124
-0.0761
-0.2571
0.2688
0.1080
t-stats
2.2585**
-2.8937**
-1.9562**
2.3321
**
MOD 3
EW
coefs
0.0105
-0.0753
-0.1988
0.2934
0.2543
0.1249
t-stats
1.8181
-2.8139**
-1.5732
2.2939
**
1.4286
MOD 4
VW
coefs
0.0079
-0.0256
-0.0072
t-stats
1.1612
-0.5052
MOD 5
VW
coefs
0.0082
-0.0248
-0.5215
-
0.0631
0.0629
t-stats
1.3270
-0.5619
-3.3129
-
0.4644
MOD 6
VW
coefs
0.0057
-0.0237
-0.4432
-
0.0301
0.3416
0.0937
t-stats
0.8463
-0.5119
-2.9746
-
0.2158
1.7287
Notes: EW: equal weighted; VW: value weighted.
Source: Authors.
For the portfolios designed with equal weights stock returns (EW), i.e., it was
observed that the capital asset pricing model (CAPM), the Fama and French (1993)
model, and the Carhart (1997) model (i.e., Models 1–3) are unable to fully explain the
variation in returns compared to the returns of the portfolios created using the growth
value investment strategy (constructed using the EM variable), which show significant
abnormal returns. In Model 1 of Panel B, there is an alpha of 1.35% per month that is
significant at 5%, which corresponds to a return of 17.46% per year. In Model 2 that
includes the SMB and HML factors, the investment strategy delivers a return of 1.24%
per month, with a t-statistic of 2.2586, i.e., an annualized return of 15.94%. The return
in Model 3, which includes the time factor (UMD), continues significantly at 10% (even
Enterprise multiple and future returns of the Brazilian stock market
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
439
though there is a reduction of the t-statistic); further, this model presents a significant
abnormal return of 1.05% per month. However, for the portfolios formed with
weighting, the single-factor model, the Fama and French (1993) model, and the Carhart
(1997) model are able to explain the abnormal returns of the purchase and sell invest-
ment strategy in the lower and higher quintiles of the EM, respectively.
It should be noted that, with the exception of Model 3, the results obtained for
the Brazilian market are in line with those reported for the U.S. market by Loughran
and Wellman (2012), i.e., significant abnormal returns for the portfolios formed with-
out weighting and non-significant abnormal returns for the portfolios formed with
weighting. In addition, the portfolio formed with shares from the Brazilian market
resembles the U.S. portfolio observed by Loughran and Wellman (2012) in that the size
factor displayed a significant load at the same level, although the SMB is not significant
in either case.
Another noteworthy point is that the HML factor in Loughran and Wellman
(2012) displayed a high load (4–5 times higher than that of the other factors) in all the
models (with and without weighting). However, in this study, HML did not present any
significant load in the models with weighting. Moreover, even in Model 2 (where the
portfolios were formed without weighting), the load of the HML factor was similar to
that of the other factors. This could indicate that the book-to-market and enterprise
multiple variables in the context of Brazil are not as cross-sectionally correlated as was
expected, which reinforces the results presented in Table 3.
To observe the effect of the EM factor on the portfolios, the data were reor-
ganized. The portfolios were divided into three groups according to market value and
three groups according to the EM variable, which resulted in nine portfolios. It was
decided to divide the portfolios into three groups for each of the control variables (in-
stead of five groups as is commonly used in the international literature) because of the
limited number of listed companies in Brazil, which consequently impacts the size and
composition of the portfolios. Thus, Table 5 was constructed, in which the monthly
returns are presented without weighting (Panel A) and with weighting according to the
size factor (Panel B), after the portfolios have been classified by size and EM value; the
monthly returns for July of year t to June of year t + 1 were considered.
The portfolios with a low EM value have average monthly returns that are higher
and consistent when compared to those of the portfolios with a high EM value. This
illustrates that a strategy of purchasing in low EM and selling in high EM (after control-
ling for the effect of the size of the companies) produces positive abnormal returns
during the study period. The greatest difference in returns obtained for the investment
strategy based on the EM variable (Low EM – High EM) was observed for the average
portfolio without weighting (Panel A), which reached 1.27% per month; the monthly
returns of the portfolio with low EM value were 1.50% compared to the 0.23% returns
of the EM portfolio with high EM value. In Panel B, a similar result is observed for the
weighted portfolios: the biggest difference lies in the portfolio formed by companies
classified as medium-sized firms, which showed a difference of 1.02% per month com-
pared to the EM portfolios of low and high value.
IGREJAS, R. ET AL.
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440
Table 5 – Monthly Returns for Size and Value-Growth
EM Portfolios
Size Portfolios
Low Value
Medium
High Value
Panel A. EW monthly returns
Small
1.25%
2.03%
0.70%
Medium
1.50%
0.93%
0.23%
Large
0.63%
0.34%
0.06%
Average
1.13%
1.10%
0.33%
Panel B. VW monthly returns
Small
0.82%
0.99%
-0.04%
Medium
1.28%
0.84%
0.25%
Large
0.76%
0.03%
0.31%
Average
0.95%
0.62%
0.17%
Source: Authors.
To better evaluate the results obtained for the stock portfolios based on the
IBOVESPA, the 3-factor model proposed by Fama and French (1993) was used to ob-
serve the behavior of these variables when applied to other markets.
Comparative analysis
Given the multiple applications of the Fama and French (1992) model, we expect
some consistency in the results of the regressions using stock returns obtained in this
study with respect to the results reported for other markets. Table 6 summarizes the
results when evaluating emerging markets, the U.S. market, and Brazil in isolation. The
size variable was not significant in estimating stock returns in the different periods of
study, the only exception being the U.S. market. Moreover, the book-to-market variable
was statistically significant at 5% and positive for different time periods in Barry et al.
(2002) study in the context of emerging countries as well as in Loughran and Well-
man’s (2012) study of the U.S. market. The results of both these prior studies are in line
with the results obtained for the same variable in this study for the period 2005–2013.
Table 6: Comparative Analysis of Cross-Sectional Regression Results: Stock returns (t-
statistic)
Models
Period
Country
SIZE
BTM
Our Study
2005–2013
Brazil
-1.04
2.24
Silva et al. (2012)
2004–2011
Brazil
1.39
1.32
Loughran and Wellman (2011)
1963–2009
US
–3.04
3.41
Estrada and Serra (2005)
1992–2001
30 EM countries
-1.07
-0.12
Barry et al. (2002)
1985–2000
35 EM countries
-1.51
3.37
Source: Authors.
When analyzing the results of prior studies that use cross-sectional regressions
to examine the formation of stock portfolios following the 3-factor model proposed by
Fama and French (1993), the analysis becomes a bit more complex because of differ-
ences in the observed periods. However, the statistical significance for the SMB and
HML factors in the equal weighted method showed the same sign and relevance when
compared to the significance of these factors in Loughran and Wellman’s (2012) study
of the U.S. market. Further, the SMB and HML factors in this study had significance and
Enterprise multiple and future returns of the Brazilian stock market
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
441
magnitude similar to those reported by Cakici, Fabozzi and Tan (2013) for their sample
using data from Argentina, Brazil, Chile, Colombia, and Mexico between 1991 and 2011.
Table 7: Comparative Analysis of Fama and French (1993) factors (t-statistic)
Models
Period
Country
Weighted
Method
MKT
SMB
HML
Our Study
2005-2013
Brazil
EW
-2.89
-1.96
2.33
VW
-0.56
-3.31
-0.46
Cakici et al. (2013)
1991-2011
Argentina, Brazil,
Chile, Colombia,
and Mexico
VW
1.66
1.84
2.16
18 emerging coun-
tries
VW
0.97
1.33
3.13
USA
VW
1.81
1.48
1.22
Loughran and
Wellman (2011)
1963-2009
USA
EW
-4.69
-2.92
13.26
VW
-1.17
2.98
14.53
Rogers and Securato
(2009)
2001-2006
Brazil
VW
1.63
2.25
1.24
Jung et al. (2009)
1992-2006
Korea
EW
0.36
-0.10
1.74
Iqbal and Brooks
(2007)
1999-2005
Pakistan
VW
-0.59
1.04
-1.80
Notes: EW: equal weighted; VW: value weighted.
Source: Authors.
Final considerations
The enterprise multiple (EM) variable is a relative measure of company valua-
tion that has gained relevance, mainly because of studies by Koller, Goedhart and Wes-
sels (2005) and Damodaran (2010). This variable is calculated as the ratio of the enter-
prise value (equity value + debt + preferred stock – cash) and the earnings before in-
terest, taxes, depreciation, and amortization (EBITDA). This study aimed to assess
whether the use of the EM variable as a valuation tool for estimating stock returns is
really justified.
Based on the 96 cross-sectional monthly regressions that used data pertaining to
BOVESPA stocks between January 2005 and July 2013, a negative relationship between
the EM variable and stock returns was confirmed. The EM variable proved robust and
significant, confirming the hypothesis that the expected return of stocks in Brazil dur-
ing this period would decrease with the enterprise multiple (EM). In other words,
stocks with a high EM receive a lower rate of return compared to companies with a low
EM.
In the regressions analysis using the shares of companies, the interpretation of
the book-to-market and EM coefficients becomes problematic since these tend to be
highly correlated. To work around this limitation, it becomes necessary to apply the
portfolio formation approach proposed by Fama and French (1993), since the creation
of factors inhibits correlation between variables.
Using the return of a portfolio formed according to the difference between the
returns of the lowest and highest quintiles of EM as the dependent variable, the ab-
normal return of the portfolio formed without weighting was found to be significant for
the three-factor model proposed by Fama and French (1993). This means that a strate-
gy of purchasing stocks with a low EM and selling stocks with a high EM (the assets
IGREJAS, R. ET AL.
REBRAE, Curitiba, v.10, n. 3, p. 431-443, sep./dec. 2017
442
included in the portfolio have equal weights) during this period would result in an
abnormal return of 1.24% per month and a 5% significance level. In other words, even
after controlling for the market risk (MKT), size (SMB), and book-to-market (HML)
factors, this investment strategy would achieve an annualized return of 15.94%. For
the portfolios formed with weighting, the investment strategy formed according to the
EM variable did not present significant abnormal returns, similar to what was reported
by Loughran and Wellman (2012) for the U.S. market.
By organizing the factors in a simplified panel, the difference in the returns ob-
tained for the investment strategy based on the EM variable (Low EM – High EM) was
observed. The monthly returns of the low-value EM portfolio were 1.50% as opposed
to 0.23% for the high-value EM portfolio. Similar results were observed for the
weighted portfolios, where the biggest difference lay in the portfolio formed by com-
panies classified as medium-sized, which presented a difference of 1.02% per month
between the EM portfolios of low and high value.
It can be concluded that the estimation of cross-section returns for defining in-
vestment strategies based on the multiples of companies is relevant. The importance of
the enterprise multiple (EM) factor given by (market value of the common shares +
debt + market value of the preferred shares – cash) is worth noting. This multiple has
proven extremely useful for forming an investment strategy and for evaluating the
performance of abnormal returns in event studies, including strategies for evaluating
portfolios where weighting is attributed to the assets.
The comparative analysis of the results using the results of other studies be-
comes a bit more complex because of differences in the observed periods and the tech-
niques used to identify the factors in different studies. However, it was possible to
compare the statistical significance of some of the factors using the results reported for
the U.S. market.
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Received: 12/23/2016
Approved: 06/21/2017
